Automatic detection of certain content in images and/or other forms of data is of ever-increasing importance for machine vision, security, computer-aided diagnosis and other applications. For example, automated detection of anatomic structures is an important functionality for navigating through large 3D image datasets and supporting computer-aided diagnosis (CAD).
A classifier is a mechanism that can be used to perform automatic detection in such applications. Once trained, a classifier can indicate whether an image includes a certain object, such as an anatomic structure. Based on the amount of training, a classifier can exhibit better or worse performance. With an off-line classifier, training must be done in advance of normal use of the classifier, while with an on-line classifier, training can be done concurrently with normal use of the classifier (which training is known as on-line boosting of the classifier). Because of this ability to train, during normal use, and hence continuously improve performance while being used, on-line classifiers are increasing in popularity.
However, known on-line classifiers suffer from many deficiencies. Accordingly, new on-line classifiers are desirable.